From ff1fe3e32e25069fed750cdfe3046b7d8d5a2628 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Sat, 2 Jan 2021 09:58:51 +0000 Subject: Remove padding from direct convolution - OpenCL - Refactor direct convolution for NHWC - Remove old kernels for NHWC - Change the heuristic in CLConvolutionLayer.cpp. The new direct convolution implementation is faster than FFT Resolves COMPMID-3908 Change-Id: Iee15ce7b04e21847b6eaae5c6d3c1b18180e7efc Signed-off-by: Gian Marco Iodice Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4876 Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas --- src/core/CL/cl_kernels/direct_convolution5x5.cl | 238 +----------------------- 1 file changed, 1 insertion(+), 237 deletions(-) (limited to 'src/core/CL/cl_kernels/direct_convolution5x5.cl') diff --git a/src/core/CL/cl_kernels/direct_convolution5x5.cl b/src/core/CL/cl_kernels/direct_convolution5x5.cl index e5c7a5107d..59d668f0bf 100644 --- a/src/core/CL/cl_kernels/direct_convolution5x5.cl +++ b/src/core/CL/cl_kernels/direct_convolution5x5.cl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2018 Arm Limited. + * Copyright (c) 2016-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -69,242 +69,6 @@ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ }) -#if defined(DATA_LAYOUT_NHWC) - -#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR)) - -#if STRIDE_X == 1 -#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE1_NHWC(acc, row_ptr, weights_ptr) -#elif STRIDE_X == 2 /* STRIDE_X == 1 */ -#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE2_NHWC(acc, row_ptr, weights_ptr) -#else /* STRIDE_X not equals 1 or 2 */ -#error "STRIDE_X larger than 2 is not supported" -#endif /* STRIDE_X == 2 */ - -#define CONVOLUTION1x5_STRIDE1_NHWC(acc, row_ptr, weights_ptr) \ - ({ \ - VEC_DATA_TYPE(DATA_TYPE, 8) \ - src0 = (VEC_DATA_TYPE(DATA_TYPE, 8))( \ - PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE)); \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ - PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE)); \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ - PTR_TO_VALUE(weights_ptr + 0 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 1 * weights_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(weights_ptr + 2 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE)); \ - DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE); \ - acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ - }) - -#define CONVOLUTION1x5_STRIDE2_NHWC(acc, row_ptr, weights_ptr) \ - ({ \ - VEC_DATA_TYPE(DATA_TYPE, 16) \ - src0 = (VEC_DATA_TYPE(DATA_TYPE, 16))( \ - PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 12 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 13 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 14 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 15 * src_stride_y, DATA_TYPE)); \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ - PTR_TO_VALUE(row_ptr + 16 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 17 * src_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(row_ptr + 18 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 19 * src_stride_y, DATA_TYPE)); \ - VEC_DATA_TYPE(DATA_TYPE, 4) \ - weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \ - PTR_TO_VALUE(weights_ptr + 0 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 1 * weights_stride_y, DATA_TYPE), \ - PTR_TO_VALUE(weights_ptr + 2 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE)); \ - DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE); \ - acc += src0.s02468ACE * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \ - \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \ - acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \ - }) - -/** This kernel performs a direct convolution to convolve the low three dimensions in a tensor with the NHWC data layout - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH - * @note If biases are used then -DHAS_BIAS has to be passed at compile time - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr - * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) - * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) - * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) - * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) - * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor - * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr - * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) - * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor - * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension - */ -__kernel void direct_convolution5x5_nhwc( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), -#ifdef HAS_BIAS - VECTOR_DECLARATION(biases), -#endif /* defined(HAS_BIAS) */ - unsigned int weights_stride_w) -{ - Image src = CONVERT_TO_IMAGE_STRUCT(src); - Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); - Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); - - VEC_DATA_TYPE(DATA_TYPE, 8) - values0 = 0; - - const int id0 = get_global_id(0); - const int id1 = get_global_id(1); - const int id2 = get_global_id(2); - - __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0); - __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0) - src_stride_x * id0 + ((id2 * STRIDE_Y) - PAD_TOP) * (int)src_stride_z; - - weights_addr += id0 * weights_stride_w; - -#if(PAD_TOP == 1) - const int coordy = id2 - PAD_TOP; - for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) - { - if(coordy < 0) // special case Z = -1 doesn't exists - { - //skip first row and load the two next ones - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); - } - else if(coordy == (DST_HEIGHT - PAD_TOP - 1)) - { - // special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the - // Z axis has no padding at all. - CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - } - else - { - CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); - } - src_addr += src_stride_x; - weights_addr += weights_stride_x; - } -#elif(PAD_TOP == 2) - const int coordy = id2 * STRIDE_Y; - for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) - { - if(coordy == 0) // special case Z = -2 doesn't exists - { - //skip first row and load the two next ones - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); - } - else if(coordy == 1) // special case Z = -1 doesn't exists - { - //skip first row and load the two next ones - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); - } - else if(coordy == (SRC_HEIGHT - 1)) - { - // special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the - // Z axis has no padding at all. - CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - } - else if(coordy == (SRC_HEIGHT - 2)) - { - // special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the - // Z axis has no padding at all. - CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - } - else - { - CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); - } - src_addr += src_stride_x; - weights_addr += weights_stride_x; - } - -#else /* PAD_TOP == 2 */ - for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d) - { - CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr); - CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z)); - CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z)); - src_addr += src_stride_x; - weights_addr += weights_stride_x; - } -#endif /* PAD_TOP == 1 */ - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0))); -#endif /* defined(HAS_BIAS) */ - - *((__global DATA_TYPE *)(dst.ptr + 0 * dst_stride_y)) = values0.s0; - *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values0.s1; - *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values0.s2; - *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values0.s3; - *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values0.s4; - *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values0.s5; - *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values0.s6; - *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values0.s7; -} - -#endif // defined(DATA_LAYOUT_NHWC) - /** This kernel performs a direct convolution to convolve the low three dimensions. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float -- cgit v1.2.1